Client / Swagger
→Controller
→Service
01 / Backend
SmartTask API
A task API with six operations, clear validation errors and database persistence.
22
automated tests90.91% line · 100% branch coverage
OPEN TO JUNIOR SOFTWARE ENGINEERING ROLES
Anjali Singh.
Java backend · Python testing · Full stack projects
I build APIs and tools I can explain from the first request to the failing test. Here you can see my code, the choices I made and the results I actually measured.
2026 CSE graduate · Greater Noida, India
01 / ABOUT ME
I like finding
the edge cases.
Sharda University · B.Tech CSE · 2026
I’m drawn to backend work because I like tracing a request through an API, the service logic and the database. Once the happy path works, I check bad input and failure cases too.
My projects cover Spring Boot, React/Express and Python testing. I’ve also worked on a group model-comparison project, which gave me practice adapting to a shared method and documenting the result.
See the group projectEDUCATIONB.Tech Computer ScienceSharda University · CGPA 8.1/10
FOCUSSoftware engineeringBackend · testing · full stack
02 / SELECTED WORK
Projects with
evidence behind them.
Each card opens a case study with architecture, decisions, tests and source code. Start with SmartTask for backend or CampusConnect for full stack.
01
React / Vite client
→Express REST API
→Middleware / services
02 / Full Stack
CampusConnect
A campus events app for finding events and managing them through an admin area.
40
automated testsReact client · Express API · MongoDB
YAML test data
→Validator
→TCP client
03 / Testing
Network Service Test Framework
Repeatable TCP checks with useful evidence when a connection or response fails.
34
automated tests31 unit · 3 integration tests
Synthetic logs
→Parser
→Rules / baseline
04 / Reliability
Log Anomaly Detector
A log analysis tool that turns simulated failures into reports someone can investigate.
5,000
synthetic events analyzed1,009 overlapping rule detections
Runbooks / incidents
→Chunk / embed
→FAISS index
05 / AI / ML
AI RAG Knowledge Assistant
A local support-knowledge prototype that retrieves runbook and incident context with sources.
0.92
retrieval MRR30-query local benchmark · 18 tests
03 / TEAMWORK
A group project,
one shared method.
Employee Attrition Prediction was group work. We compared nine models with the same evaluation method. Working on a shared study meant adapting to the team’s approach and making the results understandable to others.
EMPLOYEE ATTRITION PREDICTIONGROUP PROJECT
Nine models, six data variants
The study used five-fold cross-validation and looked at precision, recall and F1 alongside accuracy.
MY DOCUMENTED CONTRIBUTION
I published the shared repository and updated its README. The project credits the group and keeps the method and results together.
04 / STACK
Tools I’ve used
in these projects.
Follow a project link to see how I used the tools, rather than taking a skill list on trust.
01Backend & data
See it in SmartTask02Full stack
See it in CampusConnect03Testing & reliability
See it in Network Service Tests04Retrieval
See it in RAG Knowledge Assistant05 / CONTACT
Let’s talk
about the work.
I’m applying for junior software engineering and backend roles. I can walk through the design and tests in any of these projects.
anjalisingh12721@gmail.com